class TestMinimaSearch(unittest.TestCase):
def setUp(self):
self.natoms = 6
self.system = LJClusterSENS(self.natoms, 2.5)
# create a database
self.database = self.system.create_database()
# add some minima to the database
bh = self.system.get_basinhopping(self.database, outstream=None)
while self.database.number_of_minima() < 2:
bh.run(1)
self.ndof = self.natoms * 3 - 6
self.minima_searcher = _MinimaSearcher(self.database.minima(), energy_accuracy=1e-4, compare_structures=self.system.get_compare_exact())
def test_exact(self):
for m in self.database.minima():
mret, tform = self.minima_searcher.get_minima(m.energy, m.coords)
self.assertEqual(m, mret)
def test_random(self):
for m in self.database.minima():
mret, tform = self.minima_searcher.get_minima(m.energy + np.random.uniform(-1e-4, 1e-4), m.coords)
self.assertEqual(m, mret)
def test_none(self):
for m in self.database.minima():
mret, tform = self.minima_searcher.get_minima(m.energy + 10., m.coords)
self.assertIsNone(mret)
class TestSENSExact_LJ(_test_ns_lj.TestNS_LJ):
def setUp(self):
# self.seed = 4
# np.random.seed(self.seed)
self.setUp1()
def set_up_system(self):
self.natoms = 6
self.gmin = -12.7121
self.system = LJClusterSENS(self.natoms, 2.5)
self.ndof = 3 * self.natoms - 6
def setUp1(self, nproc=1):
self.set_up_system()
self.nreplicas = 10
self.stepsize = 0.01
self.nproc = nproc
self.database = self.system.create_database()
# add some minima to the database
bh = self.system.get_basinhopping(self.database, outstream=None)
while self.database.number_of_minima() < 2:
bh.run(1)
# compute the thermodynamic information
get_thermodynamic_information(self.system, self.database)
get_all_normalmodes(self.system, self.database)
self.minima = list(self.database.minima())
assert self.database.number_of_minima(
) > 1, "%d minima" % self.database.number_of_minima()
self.mc_runner = self.system.get_mc_walker(mciter=200)
self.energy_accuracy = 1e-4
self.ns = NestedSamplingSAExact(
self.system,
self.nreplicas,
self.mc_runner,
self.minima,
self.energy_accuracy,
mindist=self.system.get_mindist(),
config_tests=self.system.get_config_tests(),
stepsize=0.1,
nproc=nproc,
verbose=True,
iprint=100)
self.Emax0 = self.ns.replicas[-1].energy
self.run_ns(max_iter=1000, Etol=.001)
def test1(self):
super(TestSENSExact_LJ, self).test1()
self.assertGreater(self.ns.count_sampled_minima, 0)
class TestSENSExact_LJ(_test_ns_lj.TestNS_LJ):
def setUp(self):
# self.seed = 4
# np.random.seed(self.seed)
self.setUp1()
def set_up_system(self):
self.natoms = 6
self.gmin = -12.7121
self.system = LJClusterSENS(self.natoms, 2.5)
self.ndof = 3*self.natoms - 6
def setUp1(self, nproc=1):
self.set_up_system()
self.nreplicas = 10
self.stepsize = 0.01
self.nproc = nproc
self.database = self.system.create_database()
# add some minima to the database
bh = self.system.get_basinhopping(self.database, outstream=None)
while self.database.number_of_minima() < 2:
bh.run(1)
# compute the thermodynamic information
get_thermodynamic_information(self.system, self.database)
get_all_normalmodes(self.system, self.database)
self.minima = list(self.database.minima())
assert self.database.number_of_minima() > 1, "%d minima" % self.database.number_of_minima()
self.mc_runner = self.system.get_mc_walker(mciter=200)
self.energy_accuracy = 1e-4
self.ns = NestedSamplingSAExact(self.system, self.nreplicas, self.mc_runner,
self.minima, self.energy_accuracy,
mindist=self.system.get_mindist(),
config_tests = self.system.get_config_tests(),
stepsize=0.1, nproc=nproc, verbose=True, iprint=100)
self.Emax0 = self.ns.replicas[-1].energy
self.run_ns(max_iter=1000, Etol=.001)
def test1(self):
super(TestSENSExact_LJ, self).test1()
self.assertGreater(self.ns.count_sampled_minima, 0)
class TestMinimaSearch(unittest.TestCase):
def setUp(self):
self.natoms = 6
self.system = LJClusterSENS(self.natoms, 2.5)
# create a database
self.database = self.system.create_database()
# add some minima to the database
bh = self.system.get_basinhopping(self.database, outstream=None)
while self.database.number_of_minima() < 2:
bh.run(1)
self.ndof = self.natoms * 3 - 6
self.minima_searcher = _MinimaSearcher(
self.database.minima(),
energy_accuracy=1e-4,
compare_structures=self.system.get_compare_exact())
def test_exact(self):
for m in self.database.minima():
mret, tform = self.minima_searcher.get_minima(m.energy, m.coords)
self.assertEqual(m, mret)
def test_random(self):
for m in self.database.minima():
mret, tform = self.minima_searcher.get_minima(
m.energy + np.random.uniform(-1e-4, 1e-4), m.coords)
self.assertEqual(m, mret)
def test_none(self):
for m in self.database.minima():
mret, tform = self.minima_searcher.get_minima(
m.energy + 10., m.coords)
self.assertIsNone(mret)
class TestSENSExact_LJ(_test_ns_lj.TestNS_LJ):
def setUp(self):
self.seed = np.random.randint(1000000)
# self.seed = 549670 # failed self.assertGreater(self.ns.count_sampled_minima, 0)
print "seed", self.seed
np.random.seed(self.seed)
self.setUp1()
def set_up_system(self):
self.natoms = 6
self.gmin = -12.7121
self.system = LJClusterSENS(self.natoms, 2.5)
self.ndof = 3*self.natoms - 6
def setUp1(self, nproc=1):
self.set_up_system()
self.nreplicas = 10# * nproc
self.stepsize = 0.01
self.nproc = nproc
self.database = self.system.create_database()
# add some minima to the database
bh = self.system.get_basinhopping(self.database, outstream=None)
while self.database.number_of_minima() < 2:
bh.run(1)
# compute the thermodynamic information
get_thermodynamic_information(self.system, self.database)
get_all_normalmodes(self.system, self.database)
self.minima = list(self.database.minima())
assert self.database.number_of_minima() > 1, "%d minima" % self.database.number_of_minima()
self.mc_runner = self.system.get_mc_walker(mciter=200)
self.energy_accuracy = 1e-4
self.hsa_sampler = HSASamplerCluster(self.minima, self.system.k, copy_minima=True,
center_minima=True,
energy_accuracy=self.energy_accuracy,
compare_structures=self.system.get_compare_exact(),
mindist=self.system.get_mindist(),
minimizer=self.system.get_minimizer(),
debug=True)
replicas = _utils.create_replicas(self.system, self.nreplicas)
potential = self.system.get_potential()
potential.get_energy = potential.getEnergy
self.ns = NestedSamplingSAExact(replicas, self.mc_runner,
self.hsa_sampler, potential,
config_tests=self.system.get_config_tests(),
nproc=nproc, verbose=True, iprint=1, debug=True)
self.Emax0 = self.ns.replicas[-1].energy
self.run_ns(max_iter=1000, Etol=.001)
def test1(self):
super(TestSENSExact_LJ, self).test1()
self.assertGreater(self.ns.number_swaps_accepted(), 0)
